vault backup: 2024-10-13 18:35:54

.obsidian/workspace.json

@@ -161,8 +161,10 @@
   },
   "active": "8d8de4cd4c80f0f8",
   "lastOpenFiles": [
-    "Biometric Systems/notes/2. Performance indexes.md",
+    "Foundation of data science/slides/notes 2.md",
+    "Foundation of data science/slides/Untitled.md",
     "Foundation of data science/notes/1 CV Basics.md",
+    "Biometric Systems/notes/2. Performance indexes.md",
     "Foundation of data science/notes",
     "Biometric Systems/slides/LEZIONE2_Indici_di_prestazione.pdf",
     "Biometric Systems/notes/1. Introduction.md",
@@ -175,8 +177,6 @@
     "Autonomous Networking/slides/3 WSN.pdf",
     "conflict-files-obsidian-git.md",
     "Autonomous Networking/slides/2 RFID.pdf",
-    "Foundation of data science/slides/notes 2.md",
-    "Foundation of data science/slides/Untitled.md",
     "Foundation of data science/slides/FDS_intro_new.pdf",
     "Foundation of data science/slides",
     "Foundation of data science",

Foundation of data science/slides/Untitled.md

@@ -1,12 +0,0 @@
-$$f[[m,n]+[m^{\prime},n^{\prime}]]=f\left\lbrack m+m^{\prime},n+n^{\prime}\right\rbrack=f\left\lbrack m,n\right\rbrack+f\left\lbrack m^{\prime},n^{\prime}\right\rbrack
-
-
-$$
-$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l]}+\sum_{k,l}{I[m'-k,n'-l]g[k,l]}$$
-
-$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l] + I[m'-k,n'-l]g[k,l]}$$
-
-$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{(I[m-k,n-l] + I[m'-k,n'-l])g[k,l]}$$
-
-$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}$$
-

Foundation of data science/slides/notes 2.md

@@ -1,47 +0,0 @@
-#### Object recognition
-Different types of recognition
-- object identification
-- object classification
-
-##### Which level is right for Object Classes?
-- Basic-Level Categories
-
-###### Challenges
-- multi-view: different view points
-- multi-class: different types of the same object (different car models)
-- varying illumination
-- ecc
-
-### Filtering basics
-- Linear filtering
-	- Gaussian filtering
-- Multi scale image representation
-	- gaussian pyramid
-- edge detection
-	- recognition using line drawings
-	- image derivatives (1st and 2nd order)
-- object instance identification using color histograms
-- performing evaluation
-
-probabilità dadi
-$Px(5) = 1/6$
-$Py(5) = 1/6$
-$Px+y(5) = ?$
-
-We can count the possible cases
-total cases: $6*6=36$
-
-
-| 6   | 7   | 8   | 9   | 10  | 11  | 12  |
-| --- | --- | --- | --- | --- | --- | --- |
-| 5   | 6   | 7   | 8   | 9   | 10  | 11  |
-| 4   | 5   | 6   | 7   | 8   | 9   | 10  |
-| 3   | 4   | 5   | 6   | 7   | 8   | 9   |
-| 2   | 3   | 4   | 5   | 6   | 7   | 8   |
-| 1   | 2   | 3   | 4   | 5   | 6   | 7   |
-|     | 1   | 2   | 3   | 4   | 5   | 6   |
-
-possible cases: $P(3)P(1)+P(2)P(2)+P(1)P(3)$
-
-
-$P[x*y](S) = $